class_docstr: AMPnetPipe is the asynchronous version of the MultiProcessPipe implementation
  which avoids the bubble issue, by using stale weights and gradients.
  The implementation closely follows the paper: https://arxiv.org/abs/1705.09786
code: def interleave(
      self,
      lm_dataloader: DataLoader,
      criterion: nn.Module,
      optimizer: Optimizer,
      transform_logger_object: Any,
      min_update_interval: int = 1,
      weight_prediction: bool = False,
  ) -> None:
      partitions = self.partitions
      n = len(partitions)
  
      # AMPnet implementation doesn't handle skip_trackers!
  
      assert self.group
      rank = self.group.rank()
  
      transport = self.pipeline.transport
      checkpoint_stop = self.pipeline.checkpoint_stop
      ampnet_event_loop = AsyncAMPnetEventLoop(
          partitions,
          self.group,
          transport,
          min_update_interval,
          weight_prediction,
          checkpoint_stop,
          self.input_device,
          self.chunks,
      )
  
      if rank == 0:
          ampnet_event_loop.event_loop_head_across_minibatches(
              lm_dataloader,
              criterion,
              optimizer,
              transform_logger_object,
          )
      elif self.final_stage:
          ampnet_event_loop.event_loop_tail_across_minibatches(
              lm_dataloader,
              criterion,
              optimizer,
              transform_logger_object,
          )
      else:
          ampnet_event_loop.event_loop_across_minibatches(
              lm_dataloader,
              criterion,
              optimizer,
              transform_logger_object,
          )
  
file_path: fairscale/fairscale/experimental/nn/ampnet_pipe/pipe.py
imports: from typing import Any
  from torch import nn
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  from fairscale.nn.pipe import AsyncPipe
  from .ampnet import AsyncAMPnetEventLoop
label_desc: Use image node_id 2 for calling the AMPnetPipe obj's underlying member method code with example usage: obj.interleave(lm_dataloader, criterion, optimizer, transform_logger_object, min_update_interval, weight_prediction) without return types